Cloud Based Document Understanding System




Cloud-based system, Document understanding, Natural language processing, Optical character recognition, OCR, Text extraction, Scalability


In recent years, the popularity of cloud-based systems has been on the rise, particularly in the field of document management. One of the main challenges in this area is the need for effective document understanding, which involves the extraction of meaningful information from unstructured data. To address this challenge, we propose a cloud-based document understanding system that leverages state-of-the-art machine learning techniques and natural language processing algorithms.

This system utilizes a combination of optical character recognition (OCR), text extraction, and machine learning models to extract and classify relevant information from documents. The system is designed to be scalable and flexible, allowing it to handle large volumes of data and adapt to different document types and formats. Additionally, our system employs advanced security measures to ensure the confidentiality and integrity of the processed data.

This cloud-based document understanding system has the potential to significantly improve document management processes in various industries, including healthcare, legal, and finance.


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W. Li, S. Neullens, M. Breier, M. Bosling, T. Pretz, and D. Merhof 2014 Text recognition for information retrieval in images of printed circuit boards. IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society. DOI:

A. Muliantara, A. Sanjaya N., M. Widiarth I., and A. Setiawan I. M. 2015 Prototype of cloud-based document management for scientific work validation. International Conference on Information & Communication Technology and Systems (ICTS). DOI:

Singh D., Saini J. P., and Chauhan D. S. 2015 Hindi character recognition using RBF neural network and directional group feature extraction technique. International Conference on Cognitive and Information Processing (CCIP). Computing DOI:

J. Pradeep, E. Srinivasan, and S. Himavathi 2012 Performance analysis of hybrid feature extraction technique for recognizing English handwritten characters. World Congress on Information and Communication Technologies. DOI:

D. Nasien, H. Haron, and S. Yuhaniz S. 2010 Support Vector Machine (SVM) for English Handwritten Character Recognition. Second International Conference on Computer Engineering and Applications. DOI:

B. Liu, X Yu., P. Zhang, A. Yu, Q. Fu, and X. Wei 2018 Supervised Deep Feature Extraction for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing, 56(4), 1909–1921. DOI:

R. Ramanathan, S. Ponmathavan, V. L. Thaneshwaran N., Nair A. S., and P. Soman K. 2009 Optical Character Recognition for English and Tamil Using Support Vector Machines. International Conference on Advances in Computing, Control, and Telecommunication

Zhang et al. (2020). "Cloud Document Management Systems: A Review of Recent Advances and Challenges".

Wang et al. (2022). "Cloud-Based Document Understanding: A Comprehensive Review and Analysis".

Lee et al. (2021). "Cloud-Based Document Understanding: Recent Advances and Future Directions".




How to Cite

P. Rewoo, A. K. Jaiswal, D. Mahajan, and H. Naidu, “Cloud Based Document Understanding System”, EAI Endorsed Trans IoT, vol. 10, Mar. 2024.